Quantum-cascade-laser-based detector for proximal screening of surface contaminants

Technology testing has been used to demonstrate a new sensor system that is applicable for high-throughput security screening of people and vehicles.
15 August 2016
Julia R. Dupuis and William J. Marinelli

To search for explosives, chemical warfare agents (CWAs), and toxic industrial chemicals and materials (TICs and TIMs), a capability for standoff/proximal detection and identification of surface contaminants is required for high-throughput, covert screening of people and vehicles. However, current trace detection systems—such as those employed in airports—require direct contact (i.e., swabbing). In addition, these methods are neither covert nor high throughput. In contrast, an ideal standoff detection approach should enable screening that is eye-safe, wide-area, and has a high aerial-coverage rate. It should also have sensitivity and specificity that are comparable with currently available contact sensors.

Purchase SPIE Field Guide to IR Systems, Detectors and FPAsPreviously developed standoff (tens of meters) and/or proximal (about 1m) trace surface-residue detectors are based on optical methods. These standoff optical techniques include Raman,1 quantum-cascade-laser-based (QCL-based) reflectance,2 and QCL-based photothermal3 spectroscopy, but they are associated with a number of problems. For instance, they have limited sensitivity and specificity levels (particularly in real-life settings). The sensitivity required to detect trace levels of residues at standoff ranges is also typically at odds with high area-coverage rates. In addition, very bright, directional, non-eye-safe, and almost exclusively coherent illumination sources are required to achieve high sensitivity. This use of coherent illumination, however, typically results in a speckle-noise limit from real-world surfaces. Indeed, the speckle-noise limit is often much worse than the detector-noise limit. Lastly, the extraction of very weak target signatures from a complex scene requires sophisticated detection algorithms that are capable of dynamically compensating for real-world clutter.

In our work at Physical Sciences Inc. (PSI), we have successfully addressed all the shortcomings of current trace surface residue detectors with a new sensor platform that is based on tunable QCLs. Our long-wave IR (LWIR) QCL-based surface contaminant detector platform has been developed as part of the Joint Project Manager Next-Generation Chemical Detector program.4 We have developed our platform (illustrated in Figure 1) with several key attributes. For instance, we employ a bistatic, integrated transmitter/receiver within a monolithic package. This features a spectrally tunable external cavity (EC) QCL transmitter, which is used to flood-illuminate a 5 × 5cm region of interest at 1m standoff with eye-safe irradiance levels, as well as a broadband, uncooled microbolometer focal-plane-array imaging receiver. Another key feature is the reflective calibration target we use for illumination/collection and spatial-profile normalization. We also use an intelligent speckle-reduction technique, and a robust detection algorithm that is based on the adaptive cosine estimator (ACE).5, 6

Figure 1. Schematic illustration of the surface contaminant detector platform. EC-QCL: External-cavity quantum cascade laser. FLIR Tau: The system's receiver. OAP: Off-axis parabola transmitting optic. acq.: Acquisition.

With our platform, LWIR reflectance spectroscopy is performed at proximal ranges. During this process, the eye-safe QCL is used to actively probe—with the highest LWIR spectral brightness available—fundamental absorption features of optically thick and thin materials (including solid- and liquid-phase CWAs, TICs, TIMs, and explosives). In particular, we use a Daylight Solutions MIRcat EC-QCL that covers the 900–1300cm−1 spectral range, with a minimum power of 100mW. We chose to use this QCL in concert with an FLIR Tau 336 receiver so that we can achieve a detector-noise-limited signal-to-noise ratio (SNR) of 5 (relative to the discriminant reflectivity modulation that is associated with relevant target surface loading levels). This SNR of 5 is the minimum requirement for the ACE algorithm. With our variant of the ACE algorithm, we enable contaminant detection and identification in highly cluttered environments and in the SNR regime that can be achieved with the sensor for surfaces of interest.

We use speckle mitigation, via temporal and spatial diversity, to maintain the platform's sensitivity (which is limited by the QCL's spectral power and the receiver noise). Spatial diversity is the ratio of the resolving power of the transmitter aperture to that of the receiver. High spatial diversity therefore results from averaging many speckle cells with the receiver. Temporal diversity refers to the number of independent speckle patterns that are generated during the integration of the receiver.7 We achieve high temporal diversity by using a spinning reflective diffuser at the focal point of an off-axis parabola transmitting optic. In our system, the spatial and temporal diversities work together to produce a speckle-noise-limited SNR of ≥ 20 at all wavelengths, which corresponds to a noise-equivalent-reflectivity (NEρ) of 5% for full-speckle-forming surfaces. With our detector platform we can thus achieve a speckle-mitigated and detector-noise-limited performance. The sensitivity is sufficient for accurate detection and discrimination, regardless of surface coverage morphology and underlying surface reflectivity.

We have performed developmental testing to demonstrate the capabilities (including the detection algorithms) of our sensor system. We prepared test substrates by fabricating different size wells in white-painted aluminum, chemical agent resistant coated (CARC) steel—see Figure 2(a)—and high-density polyethylene (HDPE), to simulate a range of droplet sizes. We then filled the wells sequentially with four CWA simulants, including triethyl phosphate—TEP—which is a VX (i.e., Venomous Agent X) simulant. We acquired spectral images of these targets and converted them to reflectivity spectral images with a spectral image of the calibration target. Finally, we applied the ACE detection algorithm to four target compounds (reflection and absorption) and to polystyrene.

Figure 2. (a) Photograph of the chemical agent reactant coated steel sample plate with variably sized wells. Green arrows indicate the wells that were filled for the developmental testing of the sensor system. In addition, reflectivity spectral images show the detections of (b) triethyl phosphate (TEP), (c) white-painted aluminum, and (d) high-density polyethylene. TEP, tributyl phosphate (TBP), perfluorooctane (Fl5), and 24-dichlorophenoxyacetic acid (24D) simulate Venomous Agent X and Sarin, a solid chemical warfare agent, and mustard gas, respectively.

Our detection results for the three substrate materials and TEP, where the six largest wells were filled—as indicated by the arrows in Figure 2(a)—are shown in Figure 2(b–d). The detection output for the CARC steel, with detection on five of the six wells, is shown in Figure 2(b). Some optically thick and thin bleed over can also be seen in this image. As expected, the filled wells generated a specular reflection from the liquid, whereas the bleed over generated a diffuse reflection off the TEP. The diffuse reflection was from the TEP, conforming to the CARC substrate (optically thick) and/or the CARC surface itself (optically thin). The specular reflection from the meniscus of the liquid was significantly smaller than the well and created a sub-pixel detection scenario. The detection results on white-painted aluminum are shown in Figure 2(c). In this image, all the wells were detected and included the bleed over. Lastly, the results on HDPE are shown in Figure 2(d), where five of the six wells were properly detected.

In summary, we have designed and tested a spectrally tunable QCL-based detection platform for proximal solid and liquid CWA detection. For our sensor, we employ eye-safe flood-illumination with an imaging receiver. In addition—through speckle reduction—we can achieve NEρ of better than 5% with our sensor, which enables detection at the 5–10μg/cm2 surface-loading level. With the results of our developmental testing, we demonstrated CWA simulant detection in both optically thick and thin regimes for both diffuse and specular reflection scenarios that exercised the full capability of our sensor platform. We are currently applying our new understanding of speckle mitigation, and algorithms for detection in a cluttered environment, to new surface contaminant detection programs and applications. Specifically, we are developing approaches for further speckle reduction and class-based target screening for false alarm mitigation.

This work was supported by Chemring Detection Systems and PSI internal research and development funding.

Julia R. Dupuis, William J. Marinelli
Physical Sciences Inc. (PSI)
Andover, MA

Julia Dupuis is the area manager of the Optical Systems Technologies business unit at PSI. She leads programs to develop a range of spectroscopic and hyperspectroscopic sensors, including the quantum-cascade-laser-based surface contaminant detector. She received her PhD in electrical engineering from Boston University in 2009.

William Marinelli is the executive vice-president of the Defense Systems enterprise at PSI. He has been involved in a diverse range of technical areas and has more than 40 publications in scientific journals. He received his PhD in physical chemistry from the University of California, Berkeley in 1981.

1. A. J. Sedlacek, M. D. Ray, N. S. Higdon, D. A. Richter, Short-range noncontact detection of surface contamination using Raman lidar, Proc. SPIE 4577, p. 95-104, 2002. doi:10.1117/12.455727
2. J. Jarvis, F. Fuchs, S. Hugger, R. Ostendorf, L. Butschek, Q. Yang, A. Dreyhaupt, J. Grahmann, J. Wagner, Hyperspectral image analysis for standoff trace detection using IR laser spectroscopy, Proc. SPIE 9824, p. 98240V, 2016. doi:10.1117/12.2223604
3. R. Fursternberg, C. Kendziora, M. Papantonakis, S. V. Stepnowski, J. Stepnowski, V. Nguyen, M. Rake, R. A. McGill, Stand-off detection of trace explosives by infrared photo-thermal spectroscopy, IEEE Conf. Technol. Homeland Security, p. 465-471, 2009. doi:10.1109/THS.2009.5168074
4. J. R. Dupuis, J. Hensley, B. R. Cosofret, D. Konno, P. Mulhall, T. Schmit, S. Chang, M. Allen, W. J. Marinelli, QCL-based standoff and proximal chemical detectors, Proc. SPIE 9836, p. 98362F, 2016. doi:10.1117/12.2223038
5. W. J. Marinelli, R. Miyashiro, C. M. Gittins, D. Konno, S. Chang, M. Farr, B. Perkins, Improved detection and false alarm rejection for chemical vapors using passive hyperspectral imaging, Proc. SPIE 8710, p. 871006, 2013. doi:10.1117/12.2015322
6. D. Manolakis, M. Pieper, E. Truslow, T. Cooley, M. Brueggeman, S. Lipson, The remarkable success of adaptive cosine estimator in hyperspectral target detection, Proc. SPIE 8743, p. 874302, 2013. doi:10.1117/12.2015392
7. J. W. Goodman, Speckle Phenomena in Optics, Roberts & Company, 2010.
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